OpenRaiser/Intern-Atlas huggingface dataset:
A massively parallel explicit solver for elasto-dynamic problems exploiting octree meshes
Venue: arXiv cs.CE 2021
Year: 2021

Typical areas of application of explicit dynamics are impact, crash test, and most importantly, wave propagation simulations. Due to the numerically highly demanding nature of these problems, efficient automatic mesh generators and transient solvers are required. To this end, a parallel explicit solver exploiting the advantages of balanced octree meshes is introduced. To avoid the hanging nodes problem encountered in standard finite element analysis (FEA), the scaled boundary finite element method (SBFEM) is deployed as a spatial discretization scheme. Consequently, arbitrarily shaped star-convex polyhedral elements are straightforwardly generated. Considering the scaling and transformation of octree cells, the stiffness and mass matrices of a limited number of unique cell patterns are pre-computed. A recently proposed mass lumping technique is extended to 3D yielding a well-conditioned diagonal mass matrix. This enables us to leverage the advantages of explicit time integrator, i.e., it is possible to efficiently compute the nodal displacements without the need for solving a system of linear equations. We implement the proposed scheme together with a central difference method (CDM) in a distributed computing environment. The performance of our parallel explicit solver is evaluated by means of several numerical benchmark examples, including complex geometries and various practical applications. A significant speedup is observed for these examples with up to one billion of degrees of freedom and running on up to 16,384 computing cores.
Structural Adapters in Pretrained Language Models for AMR-to-text Generation
Venue: arXiv cs.CL 2021
Year: 2021

Pretrained language models (PLM) have recently advanced graph-to-text generation, where the input graph is linearized into a sequence and fed into the PLM to obtain its representation. However, efficiently encoding the graph structure in PLMs is challenging because such models were pretrained on natural language, and modeling structured data may lead to catastrophic forgetting of distributional knowledge. In this paper, we propose StructAdapt, an adapter method to encode graph structure into PLMs. Contrary to prior work, StructAdapt effectively models interactions among the nodes based on the graph connectivity, only training graph structure-aware adapter parameters. In this way, we incorporate task-specific knowledge while maintaining the topological structure of the graph. We empirically show the benefits of explicitly encoding graph structure into PLMs using StructAdapt, outperforming the state of the art on two AMR-to-text datasets, training only 5.1% of the PLM parameters.
5G from Space: An Overview of 3GPP Non-Terrestrial Networks
Venue: arXiv cs.NI 2021
Year: 2021

We provide an overview of the 3rd generation partnership project (3GPP) work on evolving the 5G wireless technology to support non-terrestrial satellite networks. Adapting 5G to support non-terrestrial networks entails a holistic design spanning across multiple areas from radio access network to services and system aspects to core and terminals. In this article, we describe the main topics of non-terrestrial networks, explain in detail the design aspects, and share various design rationales influencing standardization.
Using Grammar Patterns to Interpret Test Method Name Evolution
Venue: arXiv cs.SE 2021
Year: 2021

It is good practice to name test methods such that they are comprehensible to developers; they must be written in such a way that their purpose and functionality are clear to those who will maintain them. Unfortunately, there is little automated support for writing or maintaining the names of test methods. This can lead to inconsistent and low-quality test names and increase the maintenance cost of supporting these methods. Due to this risk, it is essential to help developers in maintaining their test method names over time. In this paper, we use grammar patterns, and how they relate to test method behavior, to understand test naming practices. This data will be used to support an automated tool for maintaining test names.
A Study on Priming Methods for Graphical Passwords
Venue: arXiv cs.CR 2021
Year: 2021

Recent work suggests that a type of nudge or priming technique called the presentation effect may potentially improve the security of PassPoints-style graphical passwords. These nudges attempt to prime or non-intrusively bias user password choices (i.e., point selections) by gradually revealing a background image from a particular edge to another edge at password creation time. We conduct a large-scale user study (n=710) to develop further insights into the presence of this effect and to perform the first evaluations of its security impacts. We explore the usability impacts of this effect using the subset (n=100) of participants who returned for all three sessions. Our usability analyses indicate that these priming techniques do not harm usability. Our security analyses reveal that the priming techniques can measurably enhance the security of graphical passwords; however, this effect is dependent on the combination of both the image and priming techniques used.
No Intruder, no Validity: Evaluation Criteria for Privacy-Preserving Text Anonymization
Venue: arXiv cs.CL 2021
Year: 2021

For sensitive text data to be shared among NLP researchers and practitioners, shared documents need to comply with data protection and privacy laws. There is hence a growing interest in automated approaches for text anonymization. However, measuring such methods' performance is challenging: missing a single identifying attribute can reveal an individual's identity. In this paper, we draw attention to this problem and argue that researchers and practitioners developing automated text anonymization systems should carefully assess whether their evaluation methods truly reflect the system's ability to protect individuals from being re-identified. We then propose TILD, a set of evaluation criteria that comprises an anonymization method's technical performance, the information loss resulting from its anonymization, and the human ability to de-anonymize redacted documents. These criteria may facilitate progress towards a standardized way for measuring anonymization performance.
On Undecided LP, Clustering and Active Learning
Venue: arXiv cs.CG 2021
Year: 2021

We study colored coverage and clustering problems. Here, we are given a colored point set where the points are covered by (unknown) $k$ clusters, which are monochromatic (i.e., all the points covered by the same cluster, have the same color). The access to the colors of the points (or even the points themselves) is provided indirectly via various queries (such as nearest neighbor, or separation queries). We show that if the number of clusters is a constant, then one can correctly deduce the color of all the points (i.e., compute a monochromatic clustering of the points) using a polylogarithmic number of queries. We investigate several variants of this problem, including Undecided Linear Programming, covering of points by $k$ monochromatic balls, covering by $k$ triangles/simplices, and terrain simplification. For the later problem, we present the first near linear time approximation algorithm. While our approximation is slightly worse than previous work, this is the first algorithm to have subquadratic complexity if the terrain has "small" complexity.
Using a Personal Health Library-Enabled mHealth Recommender System for Self-Management of Diabetes Among Underserved Populations: Use Case for Knowledge Graphs and Linked Data
Venue: arXiv cs.AI 2021
Year: 2021

Personal health libraries (PHLs) provide a single point of secure access to patients digital health data and enable the integration of knowledge stored in their digital health profiles with other sources of global knowledge. PHLs can help empower caregivers and health care providers to make informed decisions about patients health by understanding medical events in the context of their lives. This paper reports the implementation of a mobile health digital intervention that incorporates both digital health data stored in patients PHLs and other sources of contextual knowledge to deliver tailored recommendations for improving self-care behaviors in diabetic adults. We conducted a thematic assessment of patient functional and nonfunctional requirements that are missing from current EHRs based on evidence from the literature. We used the results to identify the technologies needed to address those requirements. We describe the technological infrastructures used to construct, manage, and integrate the types of knowledge stored in the PHL. We leverage the Social Linked Data (Solid) platform to design a fully decentralized and privacy-aware platform that supports interoperability and care integration. We provided an initial prototype design of a PHL and drafted a use case scenario that involves four actors to demonstrate how the proposed prototype can be used to address user requirements, including the construction and management of the PHL and its utilization for developing a mobile app that queries the knowledge stored and integrated into the PHL in a private and fully decentralized manner to provide better recommendations. The proposed PHL helps patients and their caregivers take a central role in making decisions regarding their health and equips their health care providers with informatics tools that support the collection and interpretation of the collected knowledge.
Intelligent Reflecting Surface-aided URLLC in a Factory Automation Scenario
Venue: arXiv cs.NI 2021
Year: 2021

Different from conventional wired line connections, industrial control through wireless transmission is widely regarded as a promising solution due to its reduced cost, increased long-term reliability, and enhanced reliability. However, mission-critical applications impose stringent quality of service (QoS) requirements that entail ultra-reliability low-latency communications (URLLC). The primary feature of URLLC is that the blocklength of channel codes is short, and the conventional Shannon's Capacity is not applicable. In this paper, we consider the URLLC in a factory automation (FA) scenario. Due to densely deployed equipment in FA, wireless signal are easily blocked by the obstacles. To address this issue, we propose to deploy intelligent reflecting surface (IRS) to create an alternative transmission link, which can enhance the transmission reliability. In this paper, we focus on the performance analysis for IRS-aided URLLC-enabled communications in a FA scenario. Both the average data rate (ADR) and the average decoding error probability (ADEP) are derived under finite channel blocklength for seven cases: 1) Rayleigh fading channel; 2) With direct channel link; 3) Nakagami-m fading channel; 4) Imperfect phase alignment; 5) Multiple-IRS case; 6) Rician fading channel; 7) Correlated channels. Extensive numerical results are provided to verify the accuracy of our derived results.
Graph Convolutional Network for Swahili News Classification
Venue: arXiv cs.CL 2021
Year: 2021

This work empirically demonstrates the ability of Text Graph Convolutional Network (Text GCN) to outperform traditional natural language processing benchmarks for the task of semi-supervised Swahili news classification. In particular, we focus our experimentation on the sparsely-labelled semi-supervised context which is representative of the practical constraints facing low-resourced African languages. We follow up on this result by introducing a variant of the Text GCN model which utilises a bag of words embedding rather than a naive one-hot encoding to reduce the memory footprint of Text GCN whilst demonstrating similar predictive performance.
Digital Peter: Dataset, Competition and Handwriting Recognition Methods
Venue: arXiv cs.CV 2021
Year: 2021

This paper presents a new dataset of Peter the Great's manuscripts and describes a segmentation procedure that converts initial images of documents into the lines. The new dataset may be useful for researchers to train handwriting text recognition models as a benchmark for comparing different models. It consists of 9 694 images and text files corresponding to lines in historical documents. The open machine learning competition Digital Peter was held based on the considered dataset. The baseline solution for this competition as well as more advanced methods on handwritten text recognition are described in the article. Full dataset and all code are publicly available.
On the Distribution of "Simple Stupid Bugs" in Unit Test Files: An Exploratory Study
Venue: arXiv cs.SE 2021
Year: 2021

A key aspect of ensuring the quality of a software system is the practice of unit testing. Through unit tests, developers verify the correctness of production source code, thereby verifying the system's intended behavior under test. However, unit test code is subject to issues, ranging from bugs in the code to poor test case design (i.e., test smells). In this study, we compare and contrast the occurrences of a type of single-statement-bug-fix known as "simple stupid bugs" (SStuBs) in test and non-test (i.e., production) files in popular open-source Java Maven projects. Our results show that SStuBs occur more frequently in non-test files than in test files, with most fix-related code associated with assertion statements in test files. Further, most test files exhibiting SStuBs also exhibit test smells. We envision our findings enabling tool vendors to better support developers in improving the maintenance of test suites.
Revealing the research landscape of Master's degrees via bibliometric analyses
Venue: arXiv cs.DL 2021
Year: 2021

The evolution of a Master's programme, like many other human institutions, can be viewed as a self-organising system whose underlying structures and dynamics arise primarily from the interaction of its faculty and students. Identifying these hidden properties may not be a trivial task, due to the complex behaviour implicit in such evolution. Nonetheless, we argue that the programme's body of research production (represented mainly by dissertations) can serve this purpose. Bibliometric analyses of such data can reveal insights about production growth, collaborative networks, and visual mapping of established, niche, and emerging research topics, among other facets. Thus, we propose a bibliometric workflow aimed at discovering the production dynamics, as well as the conceptual, social and intellectual structures developed by the Master's degree, in the interest of guiding decision-makers to better assess the strengths of the programme and to prioritise strategic goals. In addition, we report two case studies to illustrate the realisation of the proposed workflow. We conclude with considerations on the possible application of the approach to other academic research units.
Efficient Bayesian Optimization using Multiscale Graph Correlation
Venue: arXiv cs.LG 2021
Year: 2021

Bayesian optimization is a powerful tool to optimize a black-box function, the evaluation of which is time-consuming or costly. In this paper, we propose a new approach to Bayesian optimization called GP-MGC, which maximizes multiscale graph correlation with respect to the global maximum to determine the next query point. We present our evaluation of GP-MGC in applications involving both synthetic benchmark functions and real-world datasets and demonstrate that GP-MGC performs as well as or even better than state-of-the-art methods such as max-value entropy search and GP-UCB.
Pose-GNN : Camera Pose Estimation System Using Graph Neural Networks
Venue: arXiv cs.CV 2021
Year: 2021

We propose a novel image based localization system using graph neural networks (GNN). The pretrained ResNet50 convolutional neural network (CNN) architecture is used to extract the important features for each image. Following, the extracted features are input to GNN to find the pose of each image by either using the image features as a node in a graph and formulate the pose estimation problem as node pose regression or modelling the image features themselves as a graph and the problem becomes graph pose regression. We do an extensive comparison between the proposed two approaches and the state of the art single image localization methods and show that using GNN leads to enhanced performance for both indoor and outdoor environments.
Hybrid Precoding for mmWave V2X Doubly-Selective Multiuser MIMO Systems
Venue: arXiv cs.IT 2021
Year: 2021

Millimeter wave (mmWave) is a practical solution to provide high data rate for the vehicle-to-everything (V2X) communications. This enables the future autonomous vehicles to exchange big data with the base stations (BSs) such as the velocity and the location to improve the awareness of the advanced driving assistance system (ADAS). In this context, we consider a single-cell multiuser doubly-selective system wherein the BS simultaneously serves multiple vehicles. To accomplish this requirement, the BS is implemented in hybrid architecture to support multiple spatial streams while the vehicles have analog-only structures. In this work, we develop a low-complexity hybrid precoding algorithm wherein the design of the hybrid precoder at the BS and the analog combiner at the vehicles require small training and feedback overhead. We propose a two-stage hybrid precoding algorithm wherein the first stage designs the analog beamformers as in single user scenario while the second stage designs the multiuser digital precoder at the BS. In the second stage, we derive closed-form digital precoders such as Maximum Ratio Transmission (MRT), Zero-Forcing (ZF) and Minimum Mean Square Error (MMSE) as a first variant while we propose iterative digital precoder as a second variant. The design of the digital precoders for the two variants requires the limited feedback sent from the vehicles to BS. We refer to the random vector quantization (RVQ) and the beamsteering codebooks to quantize the feedbacks for variants I and II, respectively, since the perfect feedback requires long overhead and large training. We evaluate the rate loss incurred by the quantization of the digital and analog codebooks against the perfect channel state information at the transmitter (CSIT).
A Novel Framework for the Analysis of Unknown Transactions in Bitcoin: Theory, Model, and Experimental Results
Venue: arXiv cs.CR 2021
Year: 2021

Bitcoin (BTC) is probably the most transparent payment network in the world, thanks to the full history of transactions available to the public. Though, Bitcoin is not a fully anonymous environment, rather a pseudonymous one, accounting for a number of attempts to beat its pseudonimity using clustering techniques. There is, however, a recurring assumption in all the cited deanonymization techniques: that each transaction output has an address attached to it. That assumption is false. An evidence is that, as of block height 591,872, there are several millions transactions with at least one output for which the Bitcoin Core client cannot infer an address. In this paper, we present a novel approach based on sound graph theory for identifying transaction inputs and outputs. Our solution implements two simple yet innovative features: it does not rely on BTC addresses and explores all the transactions stored in the blockchain. All the other existing solutions fail with respect to one or both of the cited features. In detail, we first introduce the concept of Unknown Transaction and provide a new framework to parse the Bitcoin blockchain by taking them into account. Then, we introduce a theoretical model to detect, study, and classify -- for the first time in the literature -- unknown transaction patterns in the user network. Further, in an extensive experimental campaign, we apply our model to the Bitcoin network to uncover hidden transaction patterns within the Bitcoin user network. Results are striking: we discovered more than 30,000 unknown transaction DAGs, with a few of them exhibiting a complex yet ordered topology and potentially connected to automated payment services. To the best of our knowledge, the proposed framework is the only one that enables a complete study of the unknown transaction patterns, hence enabling further research in the fields -- for which we provide some directions.
An Integration Test Order Strategy to Consider Control Coupling
Venue: arXiv cs.SE 2021
Year: 2021

Integration testing is a very important step in software testing. Existing methods evaluate the stubbing cost for class integration test orders by considering only the interclass direct relationships such as inheritance, aggregation, and association, but they omit the interclass indirect relationship caused by control coupling, which can also affect the test orders and the stubbing cost. In this paper, we introduce an integration test order strategy to consider control coupling. We advance the concept of transitive relationship to describe this kind of interclass dependency and propose a new measurement method to estimate the complexity of control coupling, which is the complexity of stubs created for a transitive relationship. We evaluate our integration test order strategy on 10 programs on various scales. The results show that considering the transitive relationship when generating class integration test orders can significantly reduce the stubbing cost for most programs and that our integration test order strategy obtains satisfactory results more quickly than other methods.
A New Approach to Enhance Security of Visual Cryptography Using Steganography (VisUS)
Venue: arXiv cs.CR 2021
Year: 2021

Steganography is a process that hides secrete message or secrete hologram or secrete video or secrete image whose mere presence within the source data should be undetectable and use for transmitting secret information over public media. Visual cryptography is a cryptographic technique in which no cryptographic computation is needed at the decryption end and the decryption is performed by the human visual system (HVS). In this paper, both Steganography and visual cryptography have been selected to provide more secure data transmission over the public media with less hazard of computation. This technique generates shares with less space overhead as well as without increasing the computational complexity compared to existing techniques and may provide better security. It is also easy to implement like other techniques of visual cryptography. Finally, experimental results are given to establish the security criteria.
Bio-Inspired Design of Artificial Striated Muscles Composed of Sarcomere-Like Contraction Units (preprint)
Venue: arXiv cs.RO 2021
Year: 2021

Biological muscles have always attracted robotics researchers due to their efficient capabilities in compliance, force generation, and mechanical work. Many groups are working on the development of artificial muscles, however, state-of-the-art methods still fall short in performance when compared with their biological counterpart. Muscles with high force output are mostly rigid, whereas traditional soft actuators take much space and are limited in strength and producing displacement. In this work, we aim to find a reasonable trade-off between these features by mimicking the striated structure of skeletal muscles. For that, we designed an artificial pneumatic myofibril composed of multiple contraction units that combine stretchable and inextensible materials. Varying the geometric parameters and the number of units in series provides flexible adjustment of the desired muscle operation. We derived a mathematical model that predicts the relationship between the input pneumatic pressure and the generated output force. A detailed experimental study is conducted to validate the performance of the proposed bio-inspired muscle.